Estimating prevalence of injecting drug users and associated heroin-related death rates in England by using regional data and incorporating prior information

Ruth King, Sheila M Bird, Antony M. Overstall, Gordon Hay, Sharon J. Hutchinson

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)
87 Downloads (Pure)

Abstract

Injecting drug users (IDUs) have a direct social and economical impact, yet can typically be regarded as a hidden population within a community. We estimate the size of the IDU population across the nine different Government Office Regions of England in 2005/6 using capture-recapture methods with age (ranging from 15-64) and gender as covariate in-formation. We consider a Bayesian model-averaging approach using log-linear models, where we are able to include explicit prior information within the analysis in relation to the total population size (elicited from the number of drug-related deaths and injectors’ drug-related death rates) and the male to female ratio of IDUs. Estimating the data at the regional level allows for regional heterogeneity and was aggregated to obtain an estimate at the England level with posterior mean of 194600 and 95% credible interval (180350, 208800), estimated to nearest 50. The results show significant regional variability in the estimated prevalence of current IDUs (with posterior means ranging from 3 to 9 per 1000 of population aged 15-64) and injecting drug-related death rates across the gender age cross-classifications.
Original languageEnglish
Pages (from-to)209-236
Number of pages28
JournalJournal of the Royal Statistical Society: Series A
Volume177
Issue number1
Early online date23 Apr 2013
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • drug-related deaths
  • log-linear models
  • population size
  • injecting drug users
  • model-averaging
  • prior information

Fingerprint

Dive into the research topics of 'Estimating prevalence of injecting drug users and associated heroin-related death rates in England by using regional data and incorporating prior information'. Together they form a unique fingerprint.

Cite this